Deep Medical Imaging, Analysis the Brest Cancer Mammography-Reference-Cited by-同舟云学术

Deep Medical Imaging, Analysis the Brest Cancer Mammography

Published:2024 Issue: Volume: Page:128-135
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:en
Short-container-title:

Author:

Rhalem Wajih,Millimono Sory,Bellarbi Larbi,Maghzaz Sarah,Alidrissi Najib,Hamdi Salsabil,Kharmoum Nassim,Benahou Ahmed,Jidane Said,Belyamani Lahcen,Ghazal Hassan,Ezziyani Mostafa

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-52388-5_14

Reference19 articles.

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2. Khrouch, S., Tsouli Fathi, M., El Yessefi, A., Cherrat, L., Rhalem, W., Ezziyyani, M.: Breast cancer progression prediction for care treatment efficiency and intelligent adaptation, based on AI algorithm classification. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds.) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. LNNS, vol. 713, pp. 1–7. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35248-5_1

3. Hossinq, M.M., Molla, S., Shamrat, F.J.M., Samia, M.M., Saha, R.M. and Khater, A.: Comparative study on breast cancer classification using multiple convolution neural network architectures IEEE, 22 (11), (2022). https://doi.org/10.1109/ICOSEC54921.2022.9951881

4. Hajiarbabi, M.: Breast cancer using deep learning and histopathology images research square. 28(03) (2023) https://doi.org/10.21203/rs.3.rs-2738738/v1

5. Shen, L., et al.: Deep learning to improve breast cancer detection on screening mammography. Sci. Reports 29(08) (2019) https://doi.org/10.1038/s41598-019-48995-4